





'''simple docstring'''






import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowerCAmelCase									=   """src/diffusers"""
lowerCAmelCase									=   """."""


# This is to make sure the diffusers module imported is the one in the repo.
lowerCAmelCase									=   importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
lowerCAmelCase									=   spec.loader.load_module()






def   __A    (				a_    :	Dict   ,a_    :	Optional[int]						):
		return line.startswith(a_						) or len(a_						) <= 1 or re.search(R"^\s*\)(\s*->.*:|:)\s*$"   ,a_						) is not None






def   __A    (				a_    :	Any						):
		lowerCAmelCase  :     Optional[Any]       						= object_name.split("."						)
		lowerCAmelCase  :     Any       						= 0

		# First let's find the module where our object lives.
		lowerCAmelCase  :     Tuple       						= parts[i]
		while i < len(a_						) and not os.path.isfile(os.path.join(a_   ,f'''{module}.py'''						)						):
				i += 1
				if i < len(a_						):
						lowerCAmelCase  :     Dict       						= os.path.join(a_   ,parts[i]						)
		if i >= len(a_						):
				raise ValueError(f'''`object_name` should begin with the name of a module of diffusers but got {object_name}.'''						)

		with open(os.path.join(a_   ,f'''{module}.py'''						)   ,"r"   ,encoding="utf-8"   ,newline="\n"						) as f:
				lowerCAmelCase  :     Optional[Any]       						= f.readlines()

		# Now let's find the class / func in the code!
		lowerCAmelCase  :     str       						= ""
		lowerCAmelCase  :     Any       						= 0
		for name in parts[i + 1 :]:
				while (
				    line_index < len(a_						) and re.search(Rf'''^{indent}(class|def)\s+{name}(\(|\:)'''   ,lines[line_index]						) is None
				):
						line_index += 1
				indent += "    "
				line_index += 1

		if line_index >= len(a_						):
				raise ValueError(f''' {object_name} does not match any function or class in {module}.'''						)

		# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
		lowerCAmelCase  :     int       						= line_index
		while line_index < len(a_						) and _should_continue(lines[line_index]   ,a_						):
				line_index += 1
		# Clean up empty lines at the end (if any).
		while len(lines[line_index - 1]						) <= 1:
				line_index -= 1

		lowerCAmelCase  :     Dict       						= lines[start_index:line_index]
		return "".join(a_						)


lowerCAmelCase									=   re.compile(R"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
lowerCAmelCase									=   re.compile(R"""^\s*(\S+)->(\S+)(\s+.*|$)""")
lowerCAmelCase									=   re.compile(R"""<FILL\s+[^>]*>""")






def   __A    (				a_    :	Optional[int]						):
		lowerCAmelCase  :     Dict       						= code.split("\n"						)
		lowerCAmelCase  :     Optional[Any]       						= 0
		while idx < len(a_						) and len(lines[idx]						) == 0:
				idx += 1
		if idx < len(a_						):
				return re.search(R"^(\s*)\S"   ,lines[idx]						).groups()[0]
		return ""






def   __A    (				a_    :	int						):
		lowerCAmelCase  :     int       						= len(get_indent(a_						)						) > 0
		if has_indent:
				lowerCAmelCase  :     Dict       						= f'''class Bla:\n{code}'''
		lowerCAmelCase  :     Optional[int]       						= black.Mode(target_versions={black.TargetVersion.PYaa}   ,line_length=1_1_9   ,preview=a_						)
		lowerCAmelCase  :     int       						= black.format_str(a_   ,mode=a_						)
		lowerCAmelCase						,		lowerCAmelCase  :     str       						= style_docstrings_in_code(a_						)
		return result[len("class Bla:\n"						) :] if has_indent else result






def   __A    (				a_    :	int   ,a_    :	Optional[int]=False						):
		with open(a_   ,"r"   ,encoding="utf-8"   ,newline="\n"						) as f:
				lowerCAmelCase  :     List[str]       						= f.readlines()
		lowerCAmelCase  :     Union[str, Any]       						= []
		lowerCAmelCase  :     Optional[Any]       						= 0
		# Not a for loop cause `lines` is going to change (if `overwrite=True`).
		while line_index < len(a_						):
				lowerCAmelCase  :     Union[str, Any]       						= _re_copy_warning.search(lines[line_index]						)
				if search is None:
						line_index += 1
						continue

				# There is some copied code here, let's retrieve the original.
				lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= search.groups()
				lowerCAmelCase  :     Optional[Any]       						= find_code_in_diffusers(a_						)
				lowerCAmelCase  :     Union[str, Any]       						= get_indent(a_						)

				lowerCAmelCase  :     Union[str, Any]       						= line_index + 1 if indent == theoretical_indent else line_index + 2
				lowerCAmelCase  :     Any       						= theoretical_indent
				lowerCAmelCase  :     List[Any]       						= start_index

				# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
				lowerCAmelCase  :     int       						= True
				while line_index < len(a_						) and should_continue:
						line_index += 1
						if line_index >= len(a_						):
								break
						lowerCAmelCase  :     List[str]       						= lines[line_index]
						lowerCAmelCase  :     Optional[Any]       						= _should_continue(a_   ,a_						) and re.search(f'''^{indent}# End copy'''   ,a_						) is None
				# Clean up empty lines at the end (if any).
				while len(lines[line_index - 1]						) <= 1:
						line_index -= 1

				lowerCAmelCase  :     Optional[int]       						= lines[start_index:line_index]
				lowerCAmelCase  :     Dict       						= "".join(a_						)

				# Remove any nested `Copied from` comments to avoid circular copies
				lowerCAmelCase  :     str       						= [line for line in theoretical_code.split("\n"						) if _re_copy_warning.search(a_						) is None]
				lowerCAmelCase  :     Optional[int]       						= "\n".join(a_						)

				# Before comparing, use the `replace_pattern` on the original code.
				if len(a_						) > 0:
						lowerCAmelCase  :     Optional[int]       						= replace_pattern.replace("with"   ,""						).split(","						)
						lowerCAmelCase  :     int       						= [_re_replace_pattern.search(a_						) for p in patterns]
						for pattern in patterns:
								if pattern is None:
										continue
								lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= pattern.groups()
								lowerCAmelCase  :     Any       						= re.sub(a_   ,a_   ,a_						)
								if option.strip() == "all-casing":
										lowerCAmelCase  :     Tuple       						= re.sub(obja.lower()   ,obja.lower()   ,a_						)
										lowerCAmelCase  :     Dict       						= re.sub(obja.upper()   ,obja.upper()   ,a_						)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
						lowerCAmelCase  :     int       						= blackify(lines[start_index - 1] + theoretical_code						)
						lowerCAmelCase  :     List[str]       						= theoretical_code[len(lines[start_index - 1]						) :]

				# Test for a diff and act accordingly.
				if observed_code != theoretical_code:
						diffs.append([object_name, start_index]						)
						if overwrite:
								lowerCAmelCase  :     Any       						= lines[:start_index] + [theoretical_code] + lines[line_index:]
								lowerCAmelCase  :     Dict       						= start_index + 1

		if overwrite and len(a_						) > 0:
				# Warn the user a file has been modified.
				print(f'''Detected changes, rewriting {filename}.'''						)
				with open(a_   ,"w"   ,encoding="utf-8"   ,newline="\n"						) as f:
						f.writelines(a_						)
		return diffs






def   __A    (				a_    :	bool = False						):
		lowerCAmelCase  :     Union[str, Any]       						= glob.glob(os.path.join(a_   ,"**/*.py"						)   ,recursive=a_						)
		lowerCAmelCase  :     str       						= []
		for filename in all_files:
				lowerCAmelCase  :     List[str]       						= is_copy_consistent(a_   ,a_						)
				diffs += [f'''- {filename}: copy does not match {d[0]} at line {d[1]}''' for d in new_diffs]
		if not overwrite and len(a_						) > 0:
				lowerCAmelCase  :     str       						= "\n".join(a_						)
				raise Exception(
				    "Found the following copy inconsistencies:\n"
				    + diff
				    + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
					lowerCAmelCase									=   parser.parse_args()

					check_copies(args.fix_and_overwrite)










'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase									=   logging.get_logger(__name__)
class 		lowerCamelCase	(   _A							):



	def __init__( self		,   a_		,   a_		,   a_		,   **a_						):
			lowerCAmelCase  :     Tuple       						= feature_size
			lowerCAmelCase  :     int       						= sampling_rate
			lowerCAmelCase  :     str       						= padding_value

			lowerCAmelCase  :     int       						= kwargs.pop("padding_side"		,   "right"						)
			lowerCAmelCase  :     int       						= kwargs.pop("return_attention_mask"		,   a_						)

			super().__init__(**a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = True		,   a_ = None		,   a_ = False		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			# If we have a list of dicts, let's convert it in a dict of lists
			# We do this to allow using this method as a collate_fn function in PyTorch Dataloader
			if isinstance(a_		,   (list, tuple)						) and isinstance(processed_features[0]		,   (dict, BatchFeature)						):
					lowerCAmelCase  :     str       						= {
					    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
					}

			# The model's main input name, usually `input_values`, has be passed for padding
			if self.model_input_names[0] not in processed_features:
					raise ValueError(
					    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
					    F''' to this method that includes {self.model_input_names[0]}, but you provided'''
					    F''' {list(processed_features.keys()						)}'''						)

			lowerCAmelCase  :     Any       						= processed_features[self.model_input_names[0]]
			lowerCAmelCase  :     Dict       						= (
			    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
			)

			if len(a_						) == 0:
					if return_attention_mask:
							lowerCAmelCase  :     List[Any]       						= []
					return processed_features

			# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
			# and rebuild them afterwards if no return_tensors is specified
			# Note that we lose the specific device the tensor may be on for PyTorch

			lowerCAmelCase  :     Optional[int]       						= required_input[0]
			if isinstance(a_		,   (list, tuple)						):
					# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
					lowerCAmelCase  :     Optional[Any]       						= 0
					while len(required_input[index]						) == 0:
							index += 1
					if index < len(a_						):
							lowerCAmelCase  :     Union[str, Any]       						= required_input[index][0]

			if return_tensors is None:
					if is_tf_tensor(a_						):
							lowerCAmelCase  :     Tuple       						= "tf"
					elif is_torch_tensor(a_						):
							lowerCAmelCase  :     str       						= "pt"
					elif isinstance(a_		,   (int, float, list, tuple, np.ndarray)						):
							lowerCAmelCase  :     Dict       						= "np"
					else:
							raise ValueError(
							    F'''type of {first_element} unknown: {type(a_						)}. '''
							    "Should be one of a python, numpy, pytorch or tensorflow object."						)

			for key, value in processed_features.items():
					if isinstance(value[0]		,   (int, float)						):
							lowerCAmelCase  :     Dict       						= to_numpy(a_						)
					else:
							lowerCAmelCase  :     Any       						= [to_numpy(a_						) for v in value]

        # Convert padding_strategy in PaddingStrategy
			lowerCAmelCase  :     Optional[int]       						= self._get_padding_strategies(padding=a_		,   max_length=a_						)

			lowerCAmelCase  :     Dict       						= processed_features[self.model_input_names[0]]

			lowerCAmelCase  :     Any       						= len(a_						)
			if not all(len(a_						) == batch_size for v in processed_features.values()						):
					raise ValueError("Some items in the output dictionary have a different batch size than others."						)

			lowerCAmelCase  :     Tuple       						= []
			for i in range(a_						):
					lowerCAmelCase  :     Any       						= {k: v[i] for k, v in processed_features.items()}
					# truncation
					lowerCAmelCase  :     Tuple       						= self._truncate(
					    a_		,   max_length=a_		,   pad_to_multiple_of=a_		,   truncation=a_		,   )
					truncated_inputs.append(a_						)

			if padding_strategy == PaddingStrategy.LONGEST:
					# make sure that `max_length` cannot be longer than the longest truncated length
					lowerCAmelCase  :     Optional[Any]       						= max(len(input_slice[self.model_input_names[0]]						) for input_slice in truncated_inputs						)
					lowerCAmelCase  :     int       						= PaddingStrategy.MAX_LENGTH

			lowerCAmelCase  :     List[Any]       						= {}
			for i in range(a_						):
					# padding
					lowerCAmelCase  :     Optional[int]       						= self._pad(
					    truncated_inputs[i]		,   max_length=a_		,   padding_strategy=a_		,   pad_to_multiple_of=a_		,   return_attention_mask=a_		,   )

					for key, value in outputs.items():
							if key not in batch_outputs:
									lowerCAmelCase  :     Dict       						= []
							if value.dtype is np.dtype(np.floataa						):
									lowerCAmelCase  :     int       						= value.astype(np.floataa						)
							batch_outputs[key].append(a_						)

			return BatchFeature(a_		,   tensor_type=a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = PaddingStrategy.DO_NOT_PAD		,   a_ = None		,   a_ = None		,   ):
			lowerCAmelCase  :     Tuple       						= processed_features[self.model_input_names[0]]

			if padding_strategy == PaddingStrategy.LONGEST:
					lowerCAmelCase  :     Dict       						= len(a_						)

			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     int       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     List[Any]       						= padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a_						) < max_length

			if return_attention_mask and "attention_mask" not in processed_features:
					lowerCAmelCase  :     Any       						= np.ones(len(a_						)		,   dtype=np.intaa						)

			if needs_to_be_padded:
					lowerCAmelCase  :     Dict       						= max_length - len(a_						)
					if self.padding_side == "right":
							if return_attention_mask:
									lowerCAmelCase  :     int       						= np.pad(
									    processed_features["attention_mask"]		,   (0, difference)						)
							lowerCAmelCase  :     Optional[int]       						= ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					elif self.padding_side == "left":
							if return_attention_mask:
									lowerCAmelCase  :     Dict       						= np.pad(
									    processed_features["attention_mask"]		,   (difference, 0)						)
							lowerCAmelCase  :     Any       						= ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
							lowerCAmelCase  :     Any       						= np.pad(
							    a_		,   a_		,   "constant"		,   constant_values=self.padding_value						)
					else:
							raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

			return processed_features



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   ):
			if not truncation:
					return processed_features
			elif truncation and max_length is None:
					raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."						)

			lowerCAmelCase  :     int       						= processed_features[self.model_input_names[0]]

			# find `max_length` that fits `pad_to_multiple_of`
			if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
					lowerCAmelCase  :     Union[str, Any]       						= ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

			lowerCAmelCase  :     Any       						= len(a_						) > max_length

			if needs_to_be_truncated:
					lowerCAmelCase  :     Optional[int]       						= processed_features[self.model_input_names[0]][:max_length]
					if "attention_mask" in processed_features:
							lowerCAmelCase  :     Any       						= processed_features["attention_mask"][:max_length]

			return processed_features





	def 	_lowerCamelCase     ( self		,   a_=False		,   a_=None						):

			# Get padding strategy
			if padding is not False:
					if padding is True:
							lowerCAmelCase  :     str       						= PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
					elif not isinstance(a_		,   a_						):
							lowerCAmelCase  :     str       						= PaddingStrategy(a_						)
					elif isinstance(a_		,   a_						):
							lowerCAmelCase  :     Optional[int]       						= padding
			else:
					lowerCAmelCase  :     Tuple       						= PaddingStrategy.DO_NOT_PAD

			# Set max length if needed
			if max_length is None:
					if padding_strategy == PaddingStrategy.MAX_LENGTH:
							raise ValueError(
							    F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''						)

        # Test if we have a padding value
			if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
					raise ValueError(
					    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
					    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."						)

			return padding_strategy



